
import pandas as pd
import pandas_profiling as pp #For exploratory data analysis
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import glob


df = pd.read_csv('heart.csv')
df


df.dtypes

age           int64
sex           int64
cp            int64
trestbps      int64
chol          int64
fbs           int64
restecg       int64
thalach       int64
exang         int64
oldpeak     float64
slope         int64
ca            int64
thal          int64
target        int64
dtype: object


pp.ProfileReport(df)

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df.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol',
              'fasting_blood_sugar', 'rest_ecg', 'max_heart_rate_achieved','exercise_induced_angina',
              'st_depression', 'st_slope', 'num_major_vessels', 'thalassemia', 'diagnosis']
df


df.loc[df['sex'] == 0, 'sex'] = 'female'
df.loc[df['sex'] == 1, 'sex'] = 'male'

df.loc[df['chest_pain_type'] == 0, 'chest_pain_type'] = 'typical angina'
df.loc[df['chest_pain_type'] == 1, 'chest_pain_type'] = 'atypical angina'
df.loc[df['chest_pain_type'] == 2, 'chest_pain_type'] = 'non-anginal pain'
df.loc[df['chest_pain_type'] == 3, 'chest_pain_type'] = 'asymptomatic'

df.loc[df['fasting_blood_sugar'] == 0, 'fasting_blood_sugar'] = 'lower than 120mg/ml'
df.loc[df['fasting_blood_sugar'] == 1, 'fasting_blood_sugar'] = 'greater than 120mg/ml'

df.loc[df['rest_ecg'] == 0, 'rest_ecg'] = 'normal'
df.loc[df['rest_ecg'] == 1, 'rest_ecg'] = 'ST-T wave abnormality'
df.loc[df['rest_ecg'] == 2, 'rest_ecg'] = 'left ventricular hypertrophy'

df.loc[df['exercise_induced_angina'] == 0, 'exercise_induced_angina'] = 'no'
df.loc[df['exercise_induced_angina'] == 1, 'exercise_induced_angina'] = 'yes'

df.loc[df['st_slope'] == 0, 'st_slope'] = 'upslope'
df.loc[df['st_slope'] == 1, 'st_slope'] = 'flat'
df.loc[df['st_slope'] == 2, 'st_slope'] = 'downslope'

df.loc[df['thalassemia'] == 1, 'thalassemia'] = 'fixed defect'
df.loc[df['thalassemia'] == 2, 'thalassemia'] = 'normal'
df.loc[df['thalassemia'] == 3, 'thalassemia'] = 'reversable defect'
df


df.dtypes

age                          int64
sex                         object
chest_pain_type             object
resting_blood_pressure       int64
cholesterol                  int64
fasting_blood_sugar         object
rest_ecg                    object
max_heart_rate_achieved      int64
exercise_induced_angina     object
st_depression              float64
st_slope                    object
num_major_vessels            int64
thalassemia                 object
diagnosis                    int64
dtype: object


df = pd.get_dummies(df, drop_first=True)
df


new_cols = [col for col in df.columns if col != 'diagnosis'] + ['diagnosis']
df = df[new_cols]
df


sns.pairplot(df, hue='diagnosis')

<seaborn.axisgrid.PairGrid at 0x2adbc766608>


from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2, r_regression

data = df.copy()
X = data.iloc[:,0:19]  #independent variable columns
y = data.iloc[:,-1]    #dependent variable column 

#Apply SelectKBest to extract best features
bestfeatures = SelectKBest(score_func=chi2, k=10)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)

#Concatenate two dataframes for better visualization 
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Attributes','chi squared score']  #naming the resulting dataframe columns

print(featureScores.nlargest(19,'chi squared score'))  #print 10 best features

                                 Attributes  chi squared score
3                   max_heart_rate_achieved         650.008493
4                             st_depression         253.653461
5                         num_major_vessels         210.625919
9            chest_pain_type_typical angina         142.563300
18            thalassemia_reversable defect         141.524151
13              exercise_induced_angina_yes         130.470927
17                       thalassemia_normal         129.833983
2                               cholesterol         110.723364
0                                       age          81.425368
8          chest_pain_type_non-anginal pain          75.643418
14                            st_slope_flat          66.295938
7           chest_pain_type_atypical angina          55.917533
1                    resting_blood_pressure          45.974069
6                                  sex_male          24.373650
12                          rest_ecg_normal          13.568869
16                 thalassemia_fixed defect           8.772019
11    rest_ecg_left ventricular hypertrophy           5.888640
15                         st_slope_upslope           5.381752
10  fasting_blood_sugar_lower than 120mg/ml           0.259249


#Apply SelectKBest to extract best features
bestfeatures = SelectKBest(score_func=r_regression, k=10)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)

#Concatenate two dataframes for better visualization 
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Attributes','r squared score']  #naming the resulting dataframe columns

print(featureScores.nlargest(19,'r squared score'))  #print 10 best features

                                 Attributes  chi squared score
17                       thalassemia_normal           0.519543
3                   max_heart_rate_achieved           0.422895
8          chest_pain_type_non-anginal pain           0.319504
7           chest_pain_type_atypical angina           0.255288
10  fasting_blood_sugar_lower than 120mg/ml           0.041164
15                         st_slope_upslope          -0.075227
11    rest_ecg_left ventricular hypertrophy          -0.076357
16                 thalassemia_fixed defect          -0.095541
2                               cholesterol          -0.099966
1                    resting_blood_pressure          -0.138772
12                          rest_ecg_normal          -0.160308
0                                       age          -0.229324
6                                  sex_male          -0.279501
14                            st_slope_flat          -0.349417
5                         num_major_vessels          -0.382085
13              exercise_induced_angina_yes          -0.438029
4                             st_depression          -0.438441
18            thalassemia_reversable defect          -0.479709
9            chest_pain_type_typical angina          -0.519621


cols_to_keep = ['max_heart_rate_achieved', 'st_depression', 'num_major_vessels']
x = df[cols_to_drop]
x


y = df['diagnosis']
y

0       0
1       0
2       0
3       0
4       0
       ..
1020    1
1021    0
1022    0
1023    1
1024    0
Name: diagnosis, Length: 1025, dtype: int64


from sklearn.model_selection import train_test_split #for data splitting
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42)

#Normalize independent variable data for both sets
X_train = (X_train-np.min(X_train))/(np.max(X_train)-np.min(X_train)).values
X_test  = (X_test-np.min(X_test))/(np.max(X_test)-np.min(X_test)).values


# Get ML regressors and classifiers
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier 
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.gaussian_process import GaussianProcessClassifier

from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
#Get model metrics
from sklearn.metrics import confusion_matrix, mean_squared_error, r2_score, accuracy_score, classification_report


m1 = 'Logistic Regression'
lr = LogisticRegression()
model = lr.fit(X_train, y_train)
lr_predict = lr.predict(X_test)
lr_conf_matrix = confusion_matrix(y_test, lr_predict)
lr_acc_score = accuracy_score(y_test, lr_predict)
print("confussion matrix")
print(lr_conf_matrix)
print("\n")
print("Accuracy of Logistic Regression:",lr_acc_score*100,'\n')
print(classification_report(y_test,lr_predict))

confussion matrix
[[69 33]
 [23 80]]


Accuracy of Logistic Regression: 72.6829268292683 

              precision    recall  f1-score   support

           0       0.75      0.68      0.71       102
           1       0.71      0.78      0.74       103

    accuracy                           0.73       205
   macro avg       0.73      0.73      0.73       205
weighted avg       0.73      0.73      0.73       205


m2 = 'Naive Bayes'
nb = GaussianNB()
nb.fit(X_train,y_train)
nbpred = nb.predict(X_test)
nb_conf_matrix = confusion_matrix(y_test, nbpred)
nb_acc_score = accuracy_score(y_test, nbpred)
print("confussion matrix")
print(nb_conf_matrix)
print("\n")
print("Accuracy of Naive Bayes model:",nb_acc_score*100,'\n')
print(classification_report(y_test,nbpred))

confussion matrix
[[58 44]
 [27 76]]


Accuracy of Naive Bayes model: 65.3658536585366 

              precision    recall  f1-score   support

           0       0.68      0.57      0.62       102
           1       0.63      0.74      0.68       103

    accuracy                           0.65       205
   macro avg       0.66      0.65      0.65       205
weighted avg       0.66      0.65      0.65       205


m3 = 'Random Forest Classfier'
rf = RandomForestClassifier(n_estimators=20, random_state=12,max_depth=5)
rf.fit(X_train,y_train)
rf_predicted = rf.predict(X_test)
rf_conf_matrix = confusion_matrix(y_test, rf_predicted)
rf_acc_score = accuracy_score(y_test, rf_predicted)
print("confussion matrix")
print(rf_conf_matrix)
print("\n")
print("Accuracy of Random Forest:",rf_acc_score*100,'\n')
print(classification_report(y_test,rf_predicted))

confussion matrix
[[73 29]
 [15 88]]


Accuracy of Random Forest: 78.53658536585367 

              precision    recall  f1-score   support

           0       0.83      0.72      0.77       102
           1       0.75      0.85      0.80       103

    accuracy                           0.79       205
   macro avg       0.79      0.79      0.78       205
weighted avg       0.79      0.79      0.78       205


m4 = 'Extreme Gradient Boost'
xgb = XGBClassifier(learning_rate=0.01, n_estimators=25, max_depth=15,gamma=0.6, subsample=0.52,colsample_bytree=0.6,seed=27, 
                    reg_lambda=2, booster='dart', colsample_bylevel=0.6, colsample_bynode=0.5)
xgb.fit(X_train, y_train)
xgb_predicted = xgb.predict(X_test)
xgb_conf_matrix = confusion_matrix(y_test, xgb_predicted)
xgb_acc_score = accuracy_score(y_test, xgb_predicted)
print("confussion matrix")
print(xgb_conf_matrix)
print("\n")
print("Accuracy of Extreme Gradient Boost:",xgb_acc_score*100,'\n')
print(classification_report(y_test,xgb_predicted))

confussion matrix
[[73 29]
 [21 82]]


Accuracy of Extreme Gradient Boost: 75.60975609756098 

              precision    recall  f1-score   support

           0       0.78      0.72      0.74       102
           1       0.74      0.80      0.77       103

    accuracy                           0.76       205
   macro avg       0.76      0.76      0.76       205
weighted avg       0.76      0.76      0.76       205


m5 = 'K-NeighborsClassifier'
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(X_train, y_train)
knn_predicted = knn.predict(X_test)
knn_conf_matrix = confusion_matrix(y_test, knn_predicted)
knn_acc_score = accuracy_score(y_test, knn_predicted)
print("confussion matrix")
print(knn_conf_matrix)
print("\n")
print("Accuracy of K-NeighborsClassifier:",knn_acc_score*100,'\n')
print(classification_report(y_test,knn_predicted))

confussion matrix
[[80 22]
 [32 71]]


Accuracy of K-NeighborsClassifier: 73.65853658536585 

              precision    recall  f1-score   support

           0       0.71      0.78      0.75       102
           1       0.76      0.69      0.72       103

    accuracy                           0.74       205
   macro avg       0.74      0.74      0.74       205
weighted avg       0.74      0.74      0.74       205


m6 = 'DecisionTreeClassifier'
dt = DecisionTreeClassifier(criterion = 'entropy',random_state=0,max_depth = 6)
dt.fit(X_train, y_train)
dt_predicted = dt.predict(X_test)
dt_conf_matrix = confusion_matrix(y_test, dt_predicted)
dt_acc_score = accuracy_score(y_test, dt_predicted)
print("confussion matrix")
print(dt_conf_matrix)
print("\n")
print("Accuracy of DecisionTreeClassifier:",dt_acc_score*100,'\n')
print(classification_report(y_test,dt_predicted))

confussion matrix
[[88 14]
 [25 78]]


Accuracy of DecisionTreeClassifier: 80.97560975609757 

              precision    recall  f1-score   support

           0       0.78      0.86      0.82       102
           1       0.85      0.76      0.80       103

    accuracy                           0.81       205
   macro avg       0.81      0.81      0.81       205
weighted avg       0.81      0.81      0.81       205


m7 = 'GradientBoostingClassifier'
dt = GradientBoostingClassifier()
dt.fit(X_train, y_train)
dt_predicted = dt.predict(X_test)
dt_conf_matrix = confusion_matrix(y_test, dt_predicted)
dt_acc_score = accuracy_score(y_test, dt_predicted)
print("confussion matrix")
print(dt_conf_matrix)
print("\n")
print("Accuracy of GradientBoostingClassifier:",dt_acc_score*100,'\n')
print(classification_report(y_test,dt_predicted))

confussion matrix
[[85 17]
 [17 86]]


Accuracy of GradientBoostingClassifier: 83.41463414634146 

              precision    recall  f1-score   support

           0       0.83      0.83      0.83       102
           1       0.83      0.83      0.83       103

    accuracy                           0.83       205
   macro avg       0.83      0.83      0.83       205
weighted avg       0.83      0.83      0.83       205


m8 = 'GaussianProcessClassifier'
dt = GaussianProcessClassifier()
dt.fit(X_train, y_train)
dt_predicted = dt.predict(X_test)
dt_conf_matrix = confusion_matrix(y_test, dt_predicted)
dt_acc_score = accuracy_score(y_test, dt_predicted)
print("confussion matrix")
print(dt_conf_matrix)
print("\n")
print("Accuracy of GaussianProcessClassifier:",dt_acc_score*100,'\n')
print(classification_report(y_test,dt_predicted))

confussion matrix
[[67 35]
 [22 81]]


Accuracy of GaussianProcessClassifier: 72.1951219512195 

              precision    recall  f1-score   support

           0       0.75      0.66      0.70       102
           1       0.70      0.79      0.74       103

    accuracy                           0.72       205
   macro avg       0.73      0.72      0.72       205
weighted avg       0.73      0.72      0.72       205


m9 = 'SVC'
dt = SVC()
dt.fit(X_train, y_train)
dt_predicted = dt.predict(X_test)
dt_conf_matrix = confusion_matrix(y_test, dt_predicted)
dt_acc_score = accuracy_score(y_test, dt_predicted)
print("confussion matrix")
print(dt_conf_matrix)
print("\n")
print("Accuracy of SVC:",dt_acc_score*100,'\n')
print(classification_report(y_test,dt_predicted))

confussion matrix
[[78 24]
 [30 73]]


Accuracy of SVC: 73.65853658536585 

              precision    recall  f1-score   support

           0       0.72      0.76      0.74       102
           1       0.75      0.71      0.73       103

    accuracy                           0.74       205
   macro avg       0.74      0.74      0.74       205
weighted avg       0.74      0.74      0.74       205


m10 = 'MLPClassifier'
dt = MLPClassifier(max_iter = 1000, learning_rate_init=0.00001, solver='lbfgs')
dt.fit(X_train, y_train)
dt_predicted = dt.predict(X_test)
dt_conf_matrix = confusion_matrix(y_test, dt_predicted)
dt_acc_score = accuracy_score(y_test, dt_predicted)
print("confussion matrix")
print(dt_conf_matrix)
print("\n")
print("Accuracy of MLPClassifier:",dt_acc_score*100,'\n')
print(classification_report(y_test,dt_predicted))

confussion matrix
[[74 28]
 [23 80]]


Accuracy of MLPClassifier: 75.1219512195122 

              precision    recall  f1-score   support

           0       0.76      0.73      0.74       102
           1       0.74      0.78      0.76       103

    accuracy                           0.75       205
   macro avg       0.75      0.75      0.75       205
weighted avg       0.75      0.75      0.75       205


def model_assess(model, title = "Default"):
    model.fit(X_train, y_train)
    preds = model.predict(X_test)
    #print(confusion_matrix(y_test, preds))
    #print('Accuracy', title, ':', round(accuracy_score(y_test, preds), 5), '\n')
    score = round(accuracy_score(y_test, preds), 5);
    rf_results = pd.DataFrame([title,score]).transpose()
    rf_results.columns = ['Method','Training Score']
    return score, rf_results


lr = LogisticRegression()
score, lr_df = model_assess(lr, "Logistic Regression")

nb = GaussianNB()
score,nb_df = model_assess(nb, "Naive Bayes")

rf = RandomForestClassifier(n_estimators = 20, random_state = 12, max_depth = 5)
score,rf_df = model_assess(rf, "Random Forest Classfier")

xgb = XGBClassifier(learning_rate=0.01, n_estimators=25, max_depth=15,gamma=0.6,
                    subsample=0.52,colsample_bytree=0.6,seed=27, 
                    reg_lambda=2, booster='dart',
                    colsample_bylevel=0.6, colsample_bynode=0.5)
score,xgb_df = model_assess(xgb, "Extreme Gradient Boost")

knn = KNeighborsClassifier(n_neighbors=10)
score,knn_df = model_assess(lr, "K-NeighborsClassifier")

dt = DecisionTreeClassifier(criterion = 'entropy', random_state = 0, max_depth = 6)
score,dt_df = model_assess(dt, "Decision Tree Classifier")

gbc = GradientBoostingClassifier()
score,gbc_df = model_assess(gbc, "Gradient Boosting Classifier")

gp = GaussianProcessClassifier()
score,gp_df = model_assess(gp, "Gaussian Process Classifier")

svc = SVC()
score,svc_df = model_assess(svc, "SVC")

mlp = MLPClassifier(max_iter = 1000, learning_rate_init=0.001, solver='adam')
score,mlp_df = model_assess(mlp, "MLP Classifier")

score_df_list = [lr_df, nb_df, rf_df, xgb_df, knn_df, dt_df, gbc_df, gp_df, svc_df, mlp_df]
score_df_results = pd.concat(score_df_list, 
                        ignore_index=True).sort_values('Training Score',axis = 0, ascending = False)

score_df_results

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	52	1	0	125	212	0	1	168	0	1.0	2	2	3	0
1	53	1	0	140	203	1	0	155	1	3.1	0	0	3	0
2	70	1	0	145	174	0	1	125	1	2.6	0	0	3	0
3	61	1	0	148	203	0	1	161	0	0.0	2	1	3	0
4	62	0	0	138	294	1	1	106	0	1.9	1	3	2	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	1	1	140	221	0	1	164	1	0.0	2	0	2	1
1021	60	1	0	125	258	0	0	141	1	2.8	1	1	3	0
1022	47	1	0	110	275	0	0	118	1	1.0	1	1	2	0
1023	50	0	0	110	254	0	0	159	0	0.0	2	0	2	1
1024	54	1	0	120	188	0	1	113	0	1.4	1	1	3	0

	age	sex	chest_pain_type	resting_blood_pressure	cholesterol	fasting_blood_sugar	rest_ecg	max_heart_rate_achieved	exercise_induced_angina	st_depression	st_slope	num_major_vessels	thalassemia	diagnosis
0	52	1	0	125	212	0	1	168	0	1.0	2	2	3	0
1	53	1	0	140	203	1	0	155	1	3.1	0	0	3	0
2	70	1	0	145	174	0	1	125	1	2.6	0	0	3	0
3	61	1	0	148	203	0	1	161	0	0.0	2	1	3	0
4	62	0	0	138	294	1	1	106	0	1.9	1	3	2	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	1	1	140	221	0	1	164	1	0.0	2	0	2	1
1021	60	1	0	125	258	0	0	141	1	2.8	1	1	3	0
1022	47	1	0	110	275	0	0	118	1	1.0	1	1	2	0
1023	50	0	0	110	254	0	0	159	0	0.0	2	0	2	1
1024	54	1	0	120	188	0	1	113	0	1.4	1	1	3	0

	age	resting_blood_pressure	cholesterol	max_heart_rate_achieved	st_depression	num_major_vessels	diagnosis	sex_male	chest_pain_type_atypical angina	chest_pain_type_non-anginal pain	chest_pain_type_typical angina	fasting_blood_sugar_lower than 120mg/ml	rest_ecg_left ventricular hypertrophy	rest_ecg_normal	exercise_induced_angina_yes	st_slope_flat	st_slope_upslope	thalassemia_fixed defect	thalassemia_normal	thalassemia_reversable defect
0	52	125	212	168	1.0	2	0	1	0	0	1	1	0	0	0	0	0	0	0	1
1	53	140	203	155	3.1	0	0	1	0	0	1	0	0	1	1	0	1	0	0	1
2	70	145	174	125	2.6	0	0	1	0	0	1	1	0	0	1	0	1	0	0	1
3	61	148	203	161	0.0	1	0	1	0	0	1	1	0	0	0	0	0	0	0	1
4	62	138	294	106	1.9	3	0	0	0	0	1	0	0	0	0	1	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	140	221	164	0.0	0	1	1	1	0	0	1	0	0	1	0	0	0	1	0
1021	60	125	258	141	2.8	1	0	1	0	0	1	1	0	1	1	1	0	0	0	1
1022	47	110	275	118	1.0	1	0	1	0	0	1	1	0	1	1	1	0	0	1	0
1023	50	110	254	159	0.0	0	1	0	0	0	1	1	0	1	0	0	0	0	1	0
1024	54	120	188	113	1.4	1	0	1	0	0	1	1	0	0	0	1	0	0	0	1

	age	resting_blood_pressure	cholesterol	max_heart_rate_achieved	st_depression	num_major_vessels	sex_male	chest_pain_type_atypical angina	chest_pain_type_non-anginal pain	chest_pain_type_typical angina	fasting_blood_sugar_lower than 120mg/ml	rest_ecg_left ventricular hypertrophy	rest_ecg_normal	exercise_induced_angina_yes	st_slope_flat	st_slope_upslope	thalassemia_fixed defect	thalassemia_normal	thalassemia_reversable defect	diagnosis
0	52	125	212	168	1.0	2	1	0	0	1	1	0	0	0	0	0	0	0	1	0
1	53	140	203	155	3.1	0	1	0	0	1	0	0	1	1	0	1	0	0	1	0
2	70	145	174	125	2.6	0	1	0	0	1	1	0	0	1	0	1	0	0	1	0
3	61	148	203	161	0.0	1	1	0	0	1	1	0	0	0	0	0	0	0	1	0
4	62	138	294	106	1.9	3	0	0	0	1	0	0	0	0	1	0	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	140	221	164	0.0	0	1	1	0	0	1	0	0	1	0	0	0	1	0	1
1021	60	125	258	141	2.8	1	1	0	0	1	1	0	1	1	1	0	0	0	1	0
1022	47	110	275	118	1.0	1	1	0	0	1	1	0	1	1	1	0	0	1	0	0
1023	50	110	254	159	0.0	0	0	0	0	1	1	0	1	0	0	0	0	1	0	1
1024	54	120	188	113	1.4	1	1	0	0	1	1	0	0	0	1	0	0	0	1	0

	max_heart_rate_achieved	st_depression	num_major_vessels
0	168	1.0	2
1	155	3.1	0
2	125	2.6	0
3	161	0.0	1
4	106	1.9	3
...	...	...	...
1020	164	0.0	0
1021	141	2.8	1
1022	118	1.0	1
1023	159	0.0	0
1024	113	1.4	1

Predicting Heart Disease Using Machine Learning

Motivation for Studying Heart Disease

Loading the data

Exploratory Data Analysis

Age Interactions

Resting Blood Pressure Interactions

Cholesterol Interactions

Determining The Most Important Features

Building the Model

	age	sex	chest_pain_type	resting_blood_pressure	cholesterol	fasting_blood_sugar	rest_ecg	max_heart_rate_achieved	exercise_induced_angina	st_depression	st_slope	num_major_vessels	thalassemia	diagnosis
0	52	male	typical angina	125	212	lower than 120mg/ml	ST-T wave abnormality	168	no	1.0	downslope	2	reversable defect	0
1	53	male	typical angina	140	203	greater than 120mg/ml	normal	155	yes	3.1	upslope	0	reversable defect	0
2	70	male	typical angina	145	174	lower than 120mg/ml	ST-T wave abnormality	125	yes	2.6	upslope	0	reversable defect	0
3	61	male	typical angina	148	203	lower than 120mg/ml	ST-T wave abnormality	161	no	0.0	downslope	1	reversable defect	0
4	62	female	typical angina	138	294	greater than 120mg/ml	ST-T wave abnormality	106	no	1.9	flat	3	normal	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	male	atypical angina	140	221	lower than 120mg/ml	ST-T wave abnormality	164	yes	0.0	downslope	0	normal	1
1021	60	male	typical angina	125	258	lower than 120mg/ml	normal	141	yes	2.8	flat	1	reversable defect	0
1022	47	male	typical angina	110	275	lower than 120mg/ml	normal	118	yes	1.0	flat	1	normal	0
1023	50	female	typical angina	110	254	lower than 120mg/ml	normal	159	no	0.0	downslope	0	normal	1
1024	54	male	typical angina	120	188	lower than 120mg/ml	ST-T wave abnormality	113	no	1.4	flat	1	reversable defect	0

	Method	Training Score
6	Gradient Boosting Classifier	0.83415
5	Decision Tree Classifier	0.80976
2	Random Forest Classfier	0.78537
3	Extreme Gradient Boost	0.7561
9	MLP Classifier	0.74146
8	SVC	0.73659
0	Logistic Regression	0.72683
4	K-NeighborsClassifier	0.72683
7	Gaussian Process Classifier	0.72195
1	Naive Bayes	0.65366